(Similarity) Application Of N-Gram On K-Nearest Neighbor Algorithm To Sentiment Analysis Of TikTok Shop Shopping Features

Lestari, Riska Dwi Ayu and Rintyarna, Bagus Setya and Dasuki, Moh (2022) (Similarity) Application Of N-Gram On K-Nearest Neighbor Algorithm To Sentiment Analysis Of TikTok Shop Shopping Features. Institute of Computer Science (IOCS).

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Abstract

This study contains sentiment analysis on Twitter data with the direction of sentiment on the TikTokShop feature. In this study, the k nearest neighbor method is implemented in which the metric distance cosine similarity is used with the value of the nearest neighbor distance k = 3, 5, 7, and 9. In the modeling, a k-fold cross-validation scenario is used with a value of k = 10 fold. This study also uses unigram, bigram, and trigram selection features to handle imbalanced data using undersampling techniques. From the modeling results, it is found that the best modeling is the model with unigram feature selection with nearest neighbor k = 3. From this model, the average accuracy value is 89.92%, the average precision is 90.54% and the recall average is 87.37%. In the test, the results showed that the unigram feature selection had the best performance with 91% accuracy, 92% precision, and 89% recall.

Item Type:	Peer Review
Uncontrolled Keywords:	Analysist, KNN, N-Gram, Sentiment, Undersampling
Subjects:	600 Technology and Applied Science > 620 Engineering
Divisions:	Faculty of Engineering > Department of Electronics Engineering (S1)
Depositing User:	Bagus Setya Rintyarna
Date Deposited:	23 Dec 2022 01:16
Last Modified:	23 Dec 2022 01:16
URI:	http://repository.unmuhjember.ac.id/id/eprint/15711

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